{"title":"振动信号多重分形在故障诊断中的应用","authors":"Yuan Yu, Li Baoliang, Shang Jingshan, Yao Shixuan","doi":"10.1109/ICFN.2010.90","DOIUrl":null,"url":null,"abstract":"Condition monitoring of machines through the analysis of their vibrations has been recognized to be a difficult issue, essentially because of the strong nonlinearity of the vibration signals. In this paper, vibration signal multifractal method was researched. multifractal on time-frequency domain was an energy fractal method and the signal feature extraction was based on the analysis of its energy distributing. The method analyzed signal on time-frequency domain to characterize the distributing of its frequency or energy, and the signal’s feature was extracted by fractal dimension. After the signal was changed to time-frequency domain by Hilbert-Huang transform, general dimension Dq would be calculated from the signal in time-frequency domain by least square method. In the end, examples of emulator and practical application proved that this integrated method was feasible.","PeriodicalId":185491,"journal":{"name":"2010 Second International Conference on Future Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"The Application of Vibration Signal Multi-fractal in Fault Diagnosis\",\"authors\":\"Yuan Yu, Li Baoliang, Shang Jingshan, Yao Shixuan\",\"doi\":\"10.1109/ICFN.2010.90\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Condition monitoring of machines through the analysis of their vibrations has been recognized to be a difficult issue, essentially because of the strong nonlinearity of the vibration signals. In this paper, vibration signal multifractal method was researched. multifractal on time-frequency domain was an energy fractal method and the signal feature extraction was based on the analysis of its energy distributing. The method analyzed signal on time-frequency domain to characterize the distributing of its frequency or energy, and the signal’s feature was extracted by fractal dimension. After the signal was changed to time-frequency domain by Hilbert-Huang transform, general dimension Dq would be calculated from the signal in time-frequency domain by least square method. In the end, examples of emulator and practical application proved that this integrated method was feasible.\",\"PeriodicalId\":185491,\"journal\":{\"name\":\"2010 Second International Conference on Future Networks\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-01-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Second International Conference on Future Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICFN.2010.90\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Second International Conference on Future Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICFN.2010.90","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Application of Vibration Signal Multi-fractal in Fault Diagnosis
Condition monitoring of machines through the analysis of their vibrations has been recognized to be a difficult issue, essentially because of the strong nonlinearity of the vibration signals. In this paper, vibration signal multifractal method was researched. multifractal on time-frequency domain was an energy fractal method and the signal feature extraction was based on the analysis of its energy distributing. The method analyzed signal on time-frequency domain to characterize the distributing of its frequency or energy, and the signal’s feature was extracted by fractal dimension. After the signal was changed to time-frequency domain by Hilbert-Huang transform, general dimension Dq would be calculated from the signal in time-frequency domain by least square method. In the end, examples of emulator and practical application proved that this integrated method was feasible.